While traditional industrial robots participate in repetitive manufacturing processes from behind caged safety enclosures, collaborative robots (cobots) offer a highly flexible and human-interactive solution to manufacturing automation. Rather than operating from within cages, safety features such as force and proximity sensors and programmed protection zones allow cobots to work safely, close to human workers. Cobots can be configured to either stop or slow their motion if they come in contact with a human or obstacle or enter a protection zone, which may be a high pedestrian traffic area. In this way, a task can be divided into sub-processes allocated to the cobot or the human based on suitability, capability or human preference. The flexible nature of the cobot makes it ideal for low-volume, ‘just-in-time’ manufacturing; however, this requires frequent reprogramming of the cobot to adapt to the dynamic processes. This paper reviews relevant cobot programming and control methods currently used in the manufacturing industry and alternative solutions proposed in the literature published from 2018 to 2023. The paper aims to (1) study the features and characteristics of existing cobot programming and control methods and those proposed in the literature, (2) compare the complexity of the task that the cobot is to perform with the skills needed to program it, (3) determine who is the ideal person to perform the programming role, and (4) assess whether the cobot programming and control methods are suited to that person’s skillset or if another solution is needed. The study is presented as a guide for potential adopters of cobots for manufacturing and a reference for further research.

Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of cobots, programming them can be challenging for novice operators, who may lack the skills and understanding of robotics. If left with a choice between major worker upskilling or replacement and investing in expensive and complex precision cobot positioning and object-detection systems, business owners may be reluctant to embrace cobot ownership. Furthermore, if a cobot’s primary intended tasks were simple Pick-and-Place operations, the tenuous return on investment, compared to retaining current manual processes, could make cobot adoption financially impracticable. This paper proposes a low-cost cobot control system (LCCS), an intuitive cobot solution for Pick-and-Place tasks, designed for novice cobot operators. Off-the-shelf vision-based positioning solutions, priced at around $US20,000, are typically designed to be assigned to a single cobot. The LCCS comprises a Raspberry Pi, a standard USB webcam and ArUco fiducial markers, which can easily be incorporated into a multi-cobot operation, with a combined total hardware cost of around $US100. The system scales simply and economically to support an expanding operation and it is easy to use It allows a user to specify a target pick location by positioning a portable localisation scanner upon an object to be grasped by the cobot end-effector. The scanner’s integrated webcam captures the location and orientation perspective from ArUco markers affixed to predefined positions outside the cobot workspace. By pressing a switch mounted on the scanner, the user relays the captured information, converted to 3D coordinates, to the cobot controller. Finally, the cobot’s integrated processor calculates the corresponding pose using inverse kinematics, which allows the cobot to move to the target position. Subsequent actions can be pre-programmed as required, as part of the initial system configuration. Preliminary testing indicates that the proposed system provides accurate and repeatable localisation information, with a mean positional error below 3.5 mm and a mean standard deviation less than 1.8. With a hardware investment just 0.3% of the UR5e purchase price, an easy to use, customisable, and easily scalable vision-based Pick-and-Place localisation system for cobots can be implemented. It has the potential to be a reliable and robust system that significantly lowers cobot operation barriers for novice operators by alleviating the programming requirement. By reducing the reliance on experienced programmers in a production environment, cobot tasks could be deployed more rapidly and with greater flexibility.